Adaptive therapy: Leveraging eco-evolutionary principles to improve resistance management in oncology
Maximilian Strobl (1,2), Jill Gallagher (1), Mehdi Damaghi (3), Jeffrey West (1), Alexandra Martin (4), Mark Robertson-Tessi (1), Robert Gatenby (1,5), Robert Wenham (6), Philip Maini (2), Alexander Anderson (1)
(1) Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center & Research Institute, USA, (2) Wolfson Centre for Mathematical Biology, University of Oxford, UK, (3) Department of Cancer Physiology, H Lee Moffitt Cancer Center & Research Institute, USA, (4) Department of Clinical Science, H Lee Moffitt Cancer Center & Research Institute, USA, (5) Cancer Biology and Evolution Program, H Lee Moffitt Cancer Center & Research Institute, USA, (6) Gynecologic Oncology Program, H Lee Moffitt Cancer Center & Research Institute, USA
Introduction/Objectives: Improving the management of drug resistance is a key challenge in modern oncology. Currently, this problem is approached by seeking drug regimens that maximise cell kill and which upon failure to do so switch to a new agent, in the hope of finding one which the cells are still sensitive to. However, an emerging, eco-evolutionary understanding of cancer suggests that, unless the tumour can be cured, this approach falls short because it releases drug-resistant tumour cells from resource competition with sensitive cells, which otherwise constrain the outgrowth of these resistant sub-populations. Based on this understanding, so-called “adaptive therapy” (AT) has been proposed which seeks to leverage intra-tumoral competition to slow, or even revert, the expansion of drug resistant cells [1]. To do so, treatment is dynamically reduced in order to maintain the tumour burden tolerable whilst also preserving a pool of drug-sensitive cells which competitively suppress resistant cells [2,3]. The aim of this contribution is to investigate the factors under which AT will be superior to a maximum-kill approach, and to present preliminary results on developing an adaptive resistance management scheme for PARP inhibitor (PARPi) treatment of ovarian cancer.
Methods: We constructed an on-lattice, agent-based computational model (ABM; [4]) in which we assumed the tumour to be comprised of drug-sensitive and fully drug-resistant cells. Using simulations we compared the time to progression (TTP) between continuous treatment administration and an adaptive algorithm, in which treatment is withdrawn whenever the tumour burden has been reduced by 50% from its pre-treatment level (see also [3]). We studied how the benefit of AT depends on different tumour characteristics, specifically the initial tumour cell density, initial resistance fraction, resistance costs, and the rate of cell turnover. Moreover, in each case we quantified the competitive suppression of resistant cells to gain a mechanistic understanding of why TTP was improved. In the second part of this project we collected time-lapse microscopy data of the response of four ovarian cancer lines to different PARPi treatment schedules in vitro (Olaparib, AstraZeneca). Using these data we developed a 2-compartment ODE model of Olaparib PD, which we used to explore different plausible AT algorithms.
Results: Our ABM simulations show that AT can delay progression under a wide range of conditions, and we illustrate how this control is achieved through competitive suppression of the resistant cell population. We further find that the benefit of AT is maximised by high initial cell densities, low resistance fractions, the presence of resistance costs (resistant cells dividing more slowly than sensitive cells), and high cell turnover. Subsequently, we present the first steps of translating these theoretical concepts into a clinically actionable treatment algorithm to manage resistance to the PARPi Olaparib. We develop, calibrate, and validate an ODE model of Olaparib PD in vitro, and leverage this model to test different plausible adaptive algorithms, which will be tested in the wet lab in the future.
Conclusions: Our work discusses a new approach to manage drug resistance in oncology, in which treatment is adapted dynamically based on eco-evolutionary principles. We present theoretical analyses which improve our understanding of when such a strategy may be successful, and present preliminary results on the application of this approach to PARPi treatment in ovarian cancer.
References:
[1] Gatenby, R. A. (2009). A change of strategy in the war on cancer. Nature, 459(7246), 508–509. https://doi.org/10.1038/459508a
[2] Gatenby, R. A., Silva, A. S., Gillies, R. J., & Frieden, B. R. (2009). Adaptive therapy. Cancer Research, 69(11), 4894–4903. https://doi.org/10.1158/0008-5472.CAN-08-3658
[3] Zhang, J., Cunningham, J. J., Brown, J. S., & Gatenby, R. A. (2017). Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nature Communications, 8(1), 1816. https://doi.org/10.1038/s41467-017-01968-5
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